DocILE Benchmark for Document Information Localization and Extraction
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21230%2F23%3A00371807" target="_blank" >RIV/68407700:21230/23:00371807 - isvavai.cz</a>
Result on the web
<a href="https://doi.org/10.1007/978-3-031-41679-8_9" target="_blank" >https://doi.org/10.1007/978-3-031-41679-8_9</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1007/978-3-031-41679-8_9" target="_blank" >10.1007/978-3-031-41679-8_9</a>
Alternative languages
Result language
angličtina
Original language name
DocILE Benchmark for Document Information Localization and Extraction
Original language description
This paper introduces the DocILE benchmark with the largest dataset of business documents for the tasks of Key Information Localization and Extraction and Line Item Recognition. It contains 6.7k annotated business documents, 100k synthetically generated documents, and nearly 1M unlabeled documents for unsupervised pre-training. The dataset has been built with knowledge of domain- and task-specific aspects, resulting in the following key features: (i) annotations in 55 classes, which surpasses the granularity of previously published key information extraction datasets by a large margin; (ii) Line Item Recognition represents a highly practical information extraction task, where key information has to be assigned to items in a table; (iii) documents come from numerous layouts and the test set includes zero- and few-shot cases as well as layouts commonly seen in the training set. The benchmark comes with several baselines, including RoBERTa, LayoutLMv3 and DETR-based Table Transformer; applied to both tasks of the DocILE benchmark, with results shared in this paper, offering a quick starting point for future work. The dataset, baselines and supplementary material are available at https://github.com/rossumai/docile.
Czech name
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Czech description
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Classification
Type
D - Article in proceedings
CEP classification
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OECD FORD branch
10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)
Result continuities
Project
<a href="/en/project/EF16_019%2F0000765" target="_blank" >EF16_019/0000765: Research Center for Informatics</a><br>
Continuities
P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)<br>S - Specificky vyzkum na vysokych skolach
Others
Publication year
2023
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Article name in the collection
ICDAR 2023: Proceedings of the Document Analysis and Recognition, Part II
ISBN
978-3-031-41678-1
ISSN
0302-9743
e-ISSN
1611-3349
Number of pages
20
Pages from-to
147-166
Publisher name
Springer
Place of publication
Cham
Event location
San José
Event date
Aug 21, 2023
Type of event by nationality
WRD - Celosvětová akce
UT code for WoS article
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